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--- aira:start [2025/11/25 18:02] – [Schedule Autumn 2025] mtm
+++ aira:start [2025/11/25 18:03] (current) – [2025-11-20] mzk
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-==== 2025-11-20 ====
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-**Speaker**: Syed Muhammad Hamza Zaidi, PhD Candidate @  Otto von Guericke University Magdeburg
-**Title**: Localizing the invisible: Graph Neural Networks for Biomedical Signals.
-**Abstract**:
-The respiratory system can be significantly affected by thoracic injuries, which may lead to complications such as lung dysfunction. Immediate diagnosis, along with the precise location of these injuries is crucial as it allows targeted medical interventions, reduces unnecessary treatments and accelerates patient recovery. Traditional diagnostic tools like X-rays and CT scans offer high resolution imaging but rely on radiation exposure and hospital visits, making continuous monitoring impractical.
-This talk introduces a wearable sensor network coupled with Temporal Graph Neural Networks (TGCNs) to detect and localize breathing abnormalities non-invasively and in real time. By modeling the thorax as a graph of 14 IMU-based sensor nodes, the system captures both spatial dependencies and temporal dynamics of chest movement. The proposed model pinpoints the exact location and severity of breathing irregularities, such as shallow or asymmetric breathing patterns while maintaining high interpretability through spatially grounded predictions.
-The presentation will cover the underlying method, synthetic to real data strategy and the vision of a smart chest wearable system for continuous at home monitoring. Applications range from post-injury recovery and chronic disease management to early warning systems in preventive healthcare.
-**Biogram**:
-Zaidi is a PhD researcher at Otto-von-Guericke University Magdeburg, working at the Knowledge Management & Discovery Lab (KMD) under the supervision of Prof. Myra Spiliopoulou. His research focuses on graph neural networks, wearable sensor systems and feature importance for biomedical applications.
-He has developed a temporal graph neural network framework to detect and localize respiratory abnormalities using chest-mounted IMU sensor networks. Beyond respiratory monitoring, his research spans spatio-temporal deep learning, health signal processing and interpretable machine learning methods aimed at bridging the gap between AI research and clinical practice.
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 ==== 2025-11-13 ====